Mohammad Mozammal Huq, Statistics Department, Jahangirnagar University, Bangladesh
The usefulness of several machine learning models and strategies for sentiment analysis is examined in this research study. The gathered data and analysis offer insightful knowledge into the subject of sentiment analysis and its application to a significant number of unlabeled customer reviews and comments on Amazon products. To categorize the sentiment of the reviews, the paper suggests a supervised research model that includes two different feature extractors. Along with a thorough overview of pertinent literature on sentiment analysis utilizing text-based datasets, the core theory of the model, analysis techniques, and performance standards are all the experiments conducted on a small dataset yielded promising results, with an accuracy of over 82 percent achieved by the random forest model. The comparison of different data quantities using cross-validation, varied training-testing ratios, and various feature extraction methods contributed to the robustness of the findings.
Sentiment Analysis, Machine Learning, Text Classification, NLP.
Lin Zhang, Qing Li, Yang Wang, and Jingmei Zhao, Southwestern University of Finance and Economics Chengdu, Sichuan , China
Large Language Models (LLMs) have significant reasoning capabilities and can act as agents interacting with the real world. However, they are often segmented and, unlike humans, lack integrated systems for validating their thoughts and actions. This limitation often leads LLMs to encounter “local optima” in task performance. To mitigate this problem, we propose a replanning mechanism for LLM-based agents that dynamically incorporates feedback from actions and exploits implicit information not initially available in the reasoning framework. This approach effectively bridges the gap between the cognitive and action phases of LLMs. Experimental results on real world ticket booking platforms such as Ctrip.com and Booking.com show that our method exhibits greater robustness in following clear instructions, successfully completing more steps, and achieving a higher success rate in practical applications, especially in complex tasks requiring interactive reasoning and action.
LLM, Agents, Replanning, Reaction, Logical path reasoning.
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